Mapping soil conditions with mobile sensors

The increased emphasis on monitoring the number of inputs used in the turfgrass industry has increased the need for site-specific management. Precision turf management (PTM) is a new concept in the turfgrass industry and is based on precision agriculture (PA).

The objective of PA and PTM is to identify the amount of inputs needed in a specific area, and then to apply only what is needed for plant production. Both PA and PTM require intensive site-specific information from the measurement of key soil and plant properties. Mobile sensing devices, equipped with global positioning systems (GPS) to geo-reference all data points, are required to collect the data. Once data has been collected, geographic information system (GIS) methods are used to spatially analyze and display the results.

The Toro Company has developed a mobile multi-sensor machine, the Precision Sense 6000 (PS 6000). It measures soil moisture as percent volumetric water content (VWC) to a depth of 4 inches, soil salinity to a depth of 4 inches, soil compaction as penetration resistance, and plant performance using spectral reflectance calculated as normalized difference vegetative index (NDVI), which is a measure of plant density and color. Each data point is geo-referenced using GPS, which also provides elevation data used to map topographic relief of the area being sampled.

The increased focus on head injuries in athletics has led to an interest in the hardness of athletic field surfaces. The American Society for Testing and Materials (ASTM) has established methods to measure the hardness of athletic fields. The standard for synthetic athletic fields is the F355 method, while the standard for natural grass athletic fields is the Clegg Impact Soil Tester (CIST). The F355 and CIST methods allow the operator to measure hardness at specific locations on an athletic field, but it doesn’t allow the user to see the spatial variability in hardness across the entire field. A mobile device with a hammer attached to it will allow the surface hardness of an entire athletic field to be mapped.

The PS 6000 is used to measure soil VWC, soil salinity, soil compaction and turf vigor throughout turf areas.

The Toro Company is developing a Mobile Hardness Tester, a device with a 4.95-pound hammer and an attached accelerometer that is dropped from a height of 17.8 inches. The 17.8-inch height was chosen because that is the same drop height as the CIST. The hammer is attached to a guide bar to ensure that it falls perpendicular to the ground for every drop. The hammer and guide bar are attached to a rotating arm that is gear driven at a specific ground speed.

Each time the hammer impacts the ground, it measures the surface hardness by recording the GMax, which is the maximum value of “G” recorded during impact. G is the negative acceleration, or deceleration, which is measured by the accelerometer when the hammer impacts the surface. The GMax of the hammer is its peak deceleration. A harder surface causes the hammer to decelerate faster, which creates a higher peak deceleration resulting in a higher GMax.

Results and discussion

The data in Tables 1 and 2 shows each relationship between variables is weak. The values in Table 1 range from negative 1 to positive 1. When a value is a negative 1 or positive 1, it is perfectly linear and there is a strong relationship. When a value is at 0, there is no linear relationship between variables. The values in Table 2 range from 0 to 1. The numbers show how well the data fits a regression line. When a value is 1 it perfectly fits the regression line, and when a value is at 0 it doesn’t fit the regression line.

Each value in Table 1 shows the relationship between each variable isn’t strong. The strongest relationship occurs with hardness and GMax, which are using two different methods to measure the same thing. Also in Table 1, each of the values for the relationship between GMax and VWC is negative. The majority of the values aren’t strong, but it does show a negative relationship between the two variables. The negative relationship is important because it shows that when there is higher water content in the soil the surface is softer, and when there is lower water content in the soil the surface is harder.

The Mobile Hardness Tester is used to measure surface hardness across natural and synthetic turf areas.

However, in the relationship with VWC and hardness all the values are positive except for one. When each field was sampled the water content was ideal. Since each field was sampled at a time when water content was ideal, it may have made the relationship between hardness and VWC positive. This may also have an effect as to why the values for the relationships between variables are so low. Sampling fields when water content is low and when water content is high should be done to see if the relationship between each variable changes.

An example of the data, which can be viewed in Google Earth. The darker purple areas contain more water, while the lighter purple areas contain less water.

In Table 2, the R2 (which shows how well data points fit a regression line) for each relationship is weak. Since data was taken on fields with ideal water content it may have weakened the values since dry fields and saturated fields weren’t sampled. Since the R2 for each relationship is around 0, more sampling should be done on dry and saturated fields. Sampling dry, ideal and saturated athletic fields will provide a better understanding into the relationship of each variable for the two machines being used.

Further research can be done using the PS 6000 and the mobile hardness tester. Each soccer field that was tested had an irrigation system. It would be interesting to map an athletic field that doesn’t have irrigation to see the percent VWC, surface hardness and GMax values. The values measured from an athletic field without an irrigation system could then be compared to the values of an athletic field that has an irrigation system. Values measured from nonirrigated and irrigated athletic fields could help clarify the influence of VWC on surface hardness measured as GMax.

Zach Simons is a graduate student at Iowa State University, Van Cline and Troy Carson are agronomists with The Toro Company, and Nick Christians is a professor at Iowa State University. They would like to thank Kathy Rice and the rest of the Center for Advanced Turf Technology (CATT) team at The Toro Company for their support throughout the duration of this project.